2 SWS (150h mainly own work, possibly some hours of attendance)

Calculation basis: 15 weeks in a semester, including an examination week; each SWS corresponds to 60 minutes.

According to § 3 of the study and examination regulations for the Master's program in Business Administration

Against the background of continuous advances in digital technologies, competencies in Data Analytics and Data-Driven Decision Making, summarized as Data Literacy and the fundamentals of Mathematics and Statistics (Mathematical Literacy) form a fundamental framework of modern management. This innovative online course allows its participants to refresh and strengthen these competencies employing an highly individual learning scheme.

The course covers four subject areas.:

1) Fundamentals of Mathematics:

• Sums, products, sets, linear equations, inequalities
• Calculus (functions, limits, derivatives and integration)
• Linear algebra (matrix algebra and systems of linear equations)

2) Fundamentals of Statistics

• Random variables and stochastic modeling
• Estimation and test theory
• Regression modeling

3) Fundamentals of Management Science

• Modeling of optimization problems
• Introduction to algorithms, heuristics and metaheuristics
• Linear programming

4) Fundamentals of Empirical Research Methods

• Business research process
• Primary and secondary data collection methods
• Hypothesis testing

• The main objective of this course is to build up and strengthen the core competencies of mathematical literacy and data literacy
• The aim of the course is to equip students with a solid mathematical and analytical knowledge to successfully study questions and address challenges in the field of modern digital Management, like Data Analytics or Data-Driven Decision Making.
• The course prepares students for advanced courses in the field of data literacy offered in the Master’s program

• E-learning/online course (possibly accompanied by some supporting teaching sessions)
• Intensive block course (~8 weeks) with individual learning organization
• Participants take a mandatory online placement test at the beginning of the course to assess their current knowledge and competencies.
• Based on the test results, participants will get their individual learning objectives for the course.
• After the test, participants get access to a structured, comprehensive e-library with teaching videos, quizzes, tests, forums, scripts and other online materials.
• Participants learn with the provided material in a flexible manner and according to their individual needs to achieve the qualification objectives.
• Optional weekly consultation hours (online, Zoom) for organizational questions
• Optional webinars (Zoom) for revision and questions on learning material

• Portfolio examination. The final grade depends on the successful completion of e-assessments qualifying in all four subject areas of the course. 

• Alwan, L. C., Craig, B. A., and McCabe G. P. (2020). The Practice of Statistics for Business and Economics. 5th edition. Macmillan International Higher Education: New York.
• Bertsimas, D., and Tsitsiklis, J. N. (1997). Introduction to Linear Optimization. Athena Scientific: Massachussets.
• Gillard, J. (2020) A First Course in Statistical Inference. Springer: Cham.
• Luderer, B., Nollau, V., and Vetters, K. (2007). Mathematical Formulas for Economists. 3rd edition. Springer: Berlin, Germany.
• Navarro, D. (2018) Learning Statistics with R, open.umn.edu/opentextbooks/textbooks/559
• Quinlan, C., Babin, B., Carr, J. Griffin, M., and Zikmund, W. G. (2019). Business Research Methods. 2nd edition. South-Western, Cengage Learning: Andover, UK.
• Simon, C.P., and Blume, L. (1994). Mathematics for Economists. WW Norton & Co: London, UK.
• Winston, W. (2003). Operations Research: Applications and Algorithms. Brooks/Cole: Belmont